386 results about "Manufacturing quality" patented technology

The invention relates to the technical field of twinborn model simulation, and discloses a twinborn model simulation method and system for hot working of large forgings. The method comprises the following steps: collecting and preprocessing process parameters, quality data and environment information in a multi-source manner, and obtaining hot working characteristic data; performing correlation analysis to construct a process knowledge graph; calculating the distribution of a temperature field, a stress field and an organization field by using a self-sensing multi-field coupling var value neural network; comparing and analyzing to obtain deviation data and correction parameters; adjusting a network parameter optimization prediction result; and executing multi-objective optimization calculation, and generating a whole-process technological parameter and a control instruction. According to the method, full-process multi-physics field coupling calculation from smelting, casting, forging and pressing to heat treatment can be achieved, model parameters are dynamically adjusted according to real-time production data, the optimal process scheme is generated, and therefore the manufacturing quality and efficiency of large forgings are improved.

The invention discloses a casting surface treatment quality evaluation method and system, and relates to the technical field of intelligent manufacturing and quality control, and the method comprises the steps: carrying out the multi-modal data collection of a casting, and carrying out the preprocessing; analyzing the preprocessed data, and constructing a comprehensive quality evaluation data set; a quality evaluation model is constructed, abnormal conditions in surface processing are identified, and a quality evaluation result is generated; performing quality classification on the casting according to a quality evaluation result, generating an evaluation report, and providing quality state judgment and related optimization suggestions; the casting surface treatment process is adjusted and optimized, the overall manufacturing quality is improved, and continuous improvement of quality evaluation is achieved. According to the method, the detection precision of the casting surface treatment quality and the process optimization capability are improved. By adopting data-driven closed-loop control, quality abnormity can be accurately identified, optimization suggestions can be provided, intelligentization, stability improvement and long-term quality improvement of the manufacturing process are realized, the defect rate is effectively reduced, and the manufacturing consistency is improved.

The invention discloses an aluminum alloy surface defect detection method and system using deep learning, and relates to the related field of detection performed in combination with deep learning, and the method comprises the following steps: carrying out image acquisition on an aluminum alloy finished product, constructing a surface image data set to extract multi-scale surface features, and constructing a feature enhanced image set to carry out defect detection, and a defect detection result is obtained to dynamically optimize aluminum alloy production process parameters, and a process adjustment instruction is generated and fed back to a production system. The technical problems that traditional detection mostly depends on manual visual inspection or a machine vision system based on rules, imaging distortion, small defect missing detection and process adjustment are caused are solved, and the technical effects that deep learning is conducted through the double-branch network, the defect detection efficiency and the production yield are improved, and control over the aluminum alloy manufacturing quality is met are achieved.

The invention provides an abnormity early warning method and system used in a fan manufacturing process. The abnormity early warning method comprises the steps that a multi-source process data stream composed of part forming process data, structure assembling process data and performance testing process data in the fan manufacturing process is acquired; performing process feature analysis processing on the multi-source process data stream to obtain a time sequence evolution feature process coupling feature; performing combined anomaly identification processing on the time sequence evolution features and the process coupling features through a pre-deployed manufacturing anomaly diagnosis model to generate a process anomaly identification result; based on the front-back sequence relation of all the procedures in the fan manufacturing process and the abnormal probability value and the possibility level in the procedure abnormal identification result, the abnormal target procedure node and the associated procedure range of the abnormal influence are positioned; and generating an early warning instruction according to the target process node and the associated process range, and pushing the early warning instruction to a manufacturing execution system. Abnormity real-time monitoring and early warning in the fan manufacturing process are achieved, and the fan manufacturing quality and production efficiency are improved.

The invention provides a semiconductor manufacturing machine control method and system, and relates to the technical field of semiconductors, and the method comprises the steps: obtaining the operation parameters of an equipment cavity, and building a multi-objective optimization model containing the manufacturing cost, the manufacturing quality and the manufacturing efficiency. And training the model by adopting a deep reinforcement learning algorithm to generate an optimized prediction result. And performing deviation analysis on the prediction result and the target parameter, and inputting a deviation feature vector into a compensation amount calculation model constructed based on a long short-term memory network to generate a compensation control instruction. And inputting the compensation instruction into the multi-objective optimization model, calculating an optimal parameter combination by adopting a multi-objective optimization algorithm of dynamic weight distribution, and generating a parameter adjustment instruction. And finally, the adjustment instruction is sent to the execution unit, and self-adaptive feedback regulation and control in the semiconductor manufacturing process are realized, so that the manufacturing cost is reduced, and the manufacturing quality and the manufacturing efficiency are improved.

A multimodal, large language model (LLM)-powered smart service specifically tailored for manufacturing quality G-Code is described. This LLM can be considered as an information encoder that compiles the information from input data of various modalities into a shared numerical representation (or embedding). The various modalities in an example database used by the LLM and / or used to train the LLM, correspond to different representations of the part under consideration, such as design specifications, G-code, and CAD models. This embedding can then be used for different downstream tasks, such as verifying, debugging, indexing, etc., of a potentially vast set of G-Code files.

The invention discloses a steel plate welding quality monitoring and controlling method for ship machining, and relates to the technical field of welding control, the method comprises the following steps: monitoring and identifying a target steel plate welding seam through a monitoring and sensing module to obtain a welding seam characteristic sensing data stream, mapping processing analysis is conducted on the weld joint characteristic sensing data flow through a data processing multi-dimensional channel, weld joint defect characteristic parameter information is obtained, quality evaluation is conducted on the weld joint defect characteristic parameter information, a weld joint multi-dimensional evaluation quality result is obtained, control analysis is conducted on the weld joint multi-dimensional evaluation quality result through a steel plate welding strategy space, and a steel plate welding strategy space is obtained. And target welding control parameters are determined, welding control monitoring is carried out based on the target welding control parameters, steel plate welding feedback parameters are obtained, and quality optimization control is carried out on the target welding control parameters. The technical effects that intelligent and efficient monitoring of the welding quality is achieved through multiple nondestructive testing modes, the welding quality monitoring control accuracy and efficiency are improved, and then the ship steel plate manufacturing quality is ensured are achieved.

The invention discloses a wind power generation ring forging three-dimensional design and quality management system based on 5G communication, and belongs to the technical field of wind power equipment manufacturing. The system comprises a modeling module used for constructing a parameterized three-dimensional design model and outputting design parameters used for quality evaluation and defect identification; the process data acquisition module acquires process data such as temperature, pressure and vibration of the key structure area in real time; the defect identification module fuses the image and the signal data, and outputs defect types, positions and sensitivity influences thereof; the quality evaluation module performs quantitative scoring on the product quality based on the scoring function and the disturbance response parameters; the feedback regulation and control module identifies the dominant deviation and generates a regulation and control instruction when the score is abnormal; and the 5G communication module realizes high-speed data interaction between the modules. The method realizes closed-loop control of design, manufacturing, detection and evaluation, improves the manufacturing quality and efficiency of ring forgings, and is suitable for manufacturing scenes of large-size and high-reliability wind power structural members.

The invention discloses a quality detection method and system for vehicle safety belt production and a storage medium, and relates to the field of manufacturing quality control of automobile passive safety parts. The method aims at solving the problems that an existing detection means is weak in tiny defect recognition capacity, insufficient in defect evolution modeling and lack of intelligent feedback regulation and control. According to the method, multiple detection means such as medium-wave infrared thermal imaging, laser confocal microscopy, structured light three-dimensional reconstruction and capacitance sensing are fused, and multi-mode defect sensing of key parts such as a braid, an insertion area and an adhesive layer is achieved; deducing a defect evolution process and generating a high-credibility pseudo label by constructing a thermal-stress-rheology coupling finite element model; the graph neural network and the AI model are combined to complete defect type identification, risk quantification and grading, and automatic optimization of process parameters and production line control response are realized based on the quality grade, so that the detection precision, the response efficiency and the product yield are remarkably improved.

The invention relates to the technical field of artificial intelligence, and discloses a motor manufacturing quality intelligent detection system which comprises a sensor data acquisition module, a data processing module, an artificial intelligence analysis module, a decision feedback module and a report generation module. Aiming at electromagnetic interference and mechanical noise generated by multiple processes, a self-adaptive signal filtering mechanism is established, transient interference components of data collected by a sensor can be reduced in real time, the extraction integrity of key characteristic parameters such as vibration and temperature is ensured, the misjudgment risk caused by signal distortion is reduced, the problem that the omission ratio is too high in traditional detection is solved, and the detection accuracy is improved. And a dynamic association rule base is constructed, so that the system can capture early fault symptoms which cannot be recognized by a traditional single-dimensional model, prediction blind areas caused by multi-field data splitting are reduced, and the product life prediction precision is improved.

The invention belongs to the technical field of intelligent manufacturing, and particularly relates to a manufacturing quality prediction method and system based on a multi-mode sequential network and application, and the method comprises the steps: carrying out the preprocessing of the sequential data of a process manufacturing production line, obtaining a sample set, and carrying out the sequential division into a training set, a verification set and a test set; on the basis of the sample set, key features are enhanced through a frequency domain enhanced channel attention mechanism, a multi-period mode of a time sequence dependence and period sensing module is captured in combination with a multi-layer expansion convolutional network structure, and a multi-mode time sequence network model is constructed; and sequentially carrying out training set training, verification set parameter adjustment optimization and test set performance verification on the multi-modal sequential network model, and outputting a prediction result. According to the method, the deep dynamic association among the multivariable time series data can be mined, the accuracy and robustness of manufacturing quality prediction are improved, and an efficient and reliable technical scheme and an implementation path are provided for process industry quality control and intelligent optimization.

The invention discloses a manufacturing quality analysis system and method based on a large model Agent and MCP, and mainly relates to the field of intelligent manufacturing and artificial intelligence application. Comprising the following steps: S1, initializing a setting stage layer; s2, a data input stage layer; s3, a data processing stage layer; s4, an intelligent analysis stage; and S5, a result output stage. The system further comprises a large model agent Agent module. An agent Agent selection module; a data acquisition module; a data preprocessing module; and an analysis and report generation module. The method has the beneficial effects that through combined application between the large model Agent and the large model MCP, the process quality data processing analysis efficiency can be improved by more than 30%, and the quality analysis and decision suggestion output of the six-sigma black band level is provided through the AI; the limitations of an existing system in the aspects of strong manual dependence, lagging analysis timeliness, lack of quality talents and the like are solved.

The invention provides an intelligent manufacturing quality management method, system and equipment based on edge cloud collaboration, and belongs to the technical field of intelligent manufacturing. The method comprises the following steps: collecting multi-source heterogeneous data in real time through an edge computing node deployed in a production field; performing space-time alignment and feature extraction on the multi-source heterogeneous data to generate a standardized quality feature vector; inputting the quality feature vector into a pre-trained quality prediction model, outputting a real-time quality evaluation result and an abnormal probability, and determining a quality abnormal event; when a quality abnormal event is detected, a process parameter optimization scheme is generated based on simulation of a digital twin model, and operation parameters of production equipment are dynamically adjusted through an edge controller; and encrypting and storing the quality abnormal event, the adjusted process parameters and related production batch information to a block chain, and generating a non-tampering quality tracing chain. According to the invention, real-time monitoring and accurate prediction of production quality, rapid response of quality abnormity and dynamic adjustment of process parameters are realized.

The invention relates to a manufacturing process quality tracing and root cause analysis system based on a data graph, and belongs to the technical field of manufacturing process quality control. The system performs cleaning, duplicate removal and standardization processing on multi-source heterogeneous data in the whole manufacturing process through a data processing module to generate structured data; the data graph construction module fuses the manufacturing domain knowledge ontology, constructs a manufacturing process digital twin semantic model and a multi-dimensional manufacturing data graph, introduces a dynamic association weight updating mechanism and mines node potential implicit association; the quality tracing module positions map nodes based on query semantic vectors, and reconstructs an entity state change track by using a digital twin semantic model to generate a tracing link; and the root cause analysis module screens candidate abnormal factors, locates a root cause in combination with dynamic weight and multi-scene simulation verification, and generates a quality optimization scheme. According to the invention, full-link accurate tracing and root cause efficient positioning of the manufacturing process quality are realized, and the intelligence and refinement level of manufacturing quality management and control is improved.

The invention discloses a winding forming device for manufacturing a flat wire motor stator of a new energy automobile, and relates to the technical field of stator processing equipment.The winding forming device for manufacturing the flat wire motor stator of the new energy automobile comprises a welding base, and a product conveying tray mechanism is arranged on one side of the welding base; a torsion dividing working unit is arranged on the side, close to the product conveying tray mechanism, of the welding base, a product transplanting structure is arranged on the side, close to the torsion dividing working unit, of the welding base, and a positioning wire protecting disc is arranged on the side, away from the welding base, of the torsion dividing working unit. According to the winding forming device for manufacturing the flat wire motor stator of the new energy automobile, all the parts work cooperatively, stator positioning is guaranteed through the product transplanting structure, and multi-layer electric wire split twisting is achieved through the split twisting working unit; the positioning wire protection disc protects the electric wire; the product conveying tray mechanism and the product transplanting mechanism achieve product conveying and transplanting correspondingly, and the manufacturing quality and efficiency are guaranteed jointly.

The invention provides a manufacturing method of a large-span fish-belly type steel box girder, and relates to the field of steel box girder manufacturing, the large-span fish-belly type steel box girder is longitudinally segmented, the length of each section of bottom plate is shortened, and bending forming of the bottom plates is facilitated; a bottom plate welding jig frame is erected, and when reinforcing ribs are welded, a bottom plate is deformed into a target radian from an initial radian according to the welding seam shrinkage principle; a three-dimensional model is established, the error between the actual coordinate and the theoretical coordinate of the positioning point is judged, and the position of each structure is adjusted, so that the actual structure size of the steel box girder is more accurate. The manufacturing method has the effect of improving the manufacturing quality and efficiency of the fish-bellied steel box girder.

The invention provides an intelligent optimization method and system for metal additive manufacturing process parameters based on Bayesian optimization, and the method comprises the steps: testing the performance parameters of samples prepared under different additive manufacturing process parameters, and constructing a data set; based on the data set, training a single performance prediction model through a Gaussian regression algorithm, performing weighting processing on a plurality of performance parameters to obtain comprehensive performance, and training a comprehensive performance prediction model; performing Bayesian optimization by taking the comprehensive performance prediction model as an agent model; determining recommended process parameters according to a Bayesian optimization result, and predicting a predicted value of the sample under the recommended process parameters through a single performance prediction model; and based on the error between the measured value and the predicted value of the prepared sample, updating the data set and circularly carrying out model training and Bayesian optimization until the process parameters meeting the requirements are obtained. According to the method, a complex additive manufacturing process parameter space can be efficiently explored, and a plurality of conflicting additive manufacturing quality indexes are collaboratively optimized.

The invention discloses a staged quality detection method and system for SMT (Surface Mount Technology) semi-finished products, and particularly relates to the technical field of electronic manufacturing quality management.The staged quality detection method comprises the following steps: performing quality detection on each stage of a production line, recording detection results and forming a detection result set, identifying early signs of production rhythm imbalance by using time interval distribution analysis; if an imbalance sign exists, further calculating a defect frequency fluctuation index and a production line dynamic disorder index, and quantifying dynamic fluctuation and systematicness problems of an unqualified phenomenon; inputting the two indexes into a pre-trained machine learning model, and distinguishing whether the quality state of the production line is high quality or low quality through model output; a low-quality production line is optimized according to a preset adjustment strategy; the method can monitor the quality state of the production line in real time, accurately distinguish accidental problems from non-accidental problems, improve the quality management efficiency, guarantee the stable operation of the production line, and reduce the proportion of unqualified products.

The invention provides a hot rolled steel strip surface defect detection method based on target detection, and aims to solve the problems of difficulty in multi-scale small target recognition, insufficient feature fusion and complex background interference in hot rolled steel strip surface defect detection. Extracting multi-scale defect features by using wavelet convolution through decomposition, convolution and reconstruction processes; multi-scale features are adaptively fused based on channels and space attention through a dynamic feature fusion module (DFF); a dynamic hyperbolic tangent function (DyT) is used to replace a normalization layer to dynamically adjust the input range and compress extreme values to reduce the amount of calculation. The method has the effects of high detection average precision (mAP), low missing report rate and remarkably reduced calculation complexity, effectively improves the efficiency and reliability of identifying the surface defects of the hot rolled steel strip in a complex industrial environment, and is suitable for intelligent steel manufacturing quality control.

The invention provides a railway vehicle production and manufacturing digital twin system and method. A data acquisition module is used for acquiring railway vehicle production and manufacturing quality data; the data processing module is used for classifying the railway vehicle production and manufacturing quality data to obtain classified quality data; the data interface determination module is used for determining an interface form between the classified quality data and the railway vehicle production and manufacturing digital twin platform; and the twinborn model establishing module is used for designing the digital twinborn model on the railway vehicle production and manufacturing digital twinborn platform, and driving the size change of the digital twinborn model according to the classified quality data, so that the digital twinborn model is kept consistent with the classified quality data. The digital twinning platform can generate the twinning vehicle body with the current vehicle production and manufacturing data as the size, the railway vehicle production and manufacturing quality data can be checked conveniently, and important decision support is provided for production and manufacturing.

The invention discloses a metal structural member manufacturing quality online monitoring system, and particularly relates to the technical field of quality monitoring, and the system comprises an on-site perception and edge processing unit, a cloud digital twin unit, a cloud decision and optimization unit and a closed-loop control unit. The field unit collects multi-modal physical signals in real time and carries out preprocessing and defect preliminary classification based on a lightweight AI model; the digital twin unit drives a high-fidelity virtual model based on field data, and simulates a defect evolution and repair strategy through finite element simulation; the decision-making unit fuses classification and simulation results to generate an executable repair instruction, and performs collaborative optimization on an AI model and a simulation algorithm based on feedback data; the closed-loop unit controls instruction issuing and field execution, and real-time regulation and control and system self-optimization of the repair process are achieved. According to the system, full-process online monitoring, intelligent diagnosis and self-adaptive repairing of the manufacturing process are achieved, and the production quality and efficiency of the metal structural part are remarkably improved.

The invention relates to the technical field of cloud manufacturing, in particular to a cloud manufacturing quality control and defect prediction method and system based on deep learning. The method comprises the following steps: acquiring production field data in a manufacturing process through data acquisition equipment, and uploading the production field data to a cloud for next analysis; extracting information features of the production field data and performing feature fusion to obtain an information data feature set; constructing a joint deep learning prediction model, and training the joint deep learning prediction model by using the information data feature set; and completing cloud manufacturing quality control and defect prediction through the trained joint deep learning prediction model. The cloud manufacturing quality control and defect prediction are completed by extracting various characteristics of the production field data and training the joint deep learning prediction model, the quality problem and the defect prediction problem in the manufacturing process are solved, the production efficiency is effectively improved, the product quality is optimized, and the intelligent and automatic upgrading of the manufacturing process is promoted.

The invention discloses a PCB manufacturing quality detection method, system and device based on a dynamic threshold value and a medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining a plurality of split subspaces; acquiring detection data, acquiring a historical compensation coefficient, and acquiring a first real-time compensation coefficient; acquiring a data sequence of each fragment subspace according to detection data of each fragment subspace at a plurality of continuous detection time points, acquiring a trend index of each fragment subspace according to the data sequence of each fragment subspace, and acquiring a second real-time compensation coefficient according to the trend index of each fragment subspace; and obtaining a standard threshold value, obtaining a dynamic threshold value according to the standard threshold value, the first real-time compensation coefficient and the second real-time compensation coefficient, and obtaining a quality detection result according to the detection data of each fragment subspace at the current detection time point and the dynamic threshold value. The method has the advantages of dynamic threshold, accurate positioning and good detection effect.

The invention relates to the technical field of switch cabinet production, and particularly discloses a switch cabinet body plate laser cutting method, device and equipment and a medium, and the method comprises the steps that a three-dimensional design model and a two-dimensional expansion drawing of a target switch cabinet body are obtained; identifying the three-dimensional design model and extracting machining features about the cutting process; predicting the cutting deformation of the cabinet plate; according to the predicted cutting deformation, the offset of the machining features on the two-dimensional expansion graph is deduced reversely; generating a two-dimensional cutting map containing the compensated processing features; a cutting deviation factor is analyzed based on the two-dimensional cutting graph, a cutting sequence and a cutting forming path are optimized based on the cutting deviation factor, and a cutting strategy is generated; according to the method, a reliable reverse calculation mechanism is provided to compensate dimensional deviation caused by a forming process, accurate association of feature positions is ensured, and the laser cutting precision and the overall manufacturing quality of the switch cabinet body plate are remarkably improved.

The invention discloses an industrial surface defect anomaly detection method based on edge guidance. The method comprises the following steps: 1) performing positive sample collection on industrial parts produced by a factory to obtain training samples; 2) performing data preprocessing on the acquired positive sample image, randomly adding Berlin noise on the image, generating a synthetic negative sample, and using a sample pair as input of model training; 3) performing model training on the acquired sample by using E2TS-Seg to accurately segment a pseudo abnormal region of the specified industrial product; and 4) carrying out anomaly detection screening on a to-be-detected industrial product by using the trained model. Compared with traditional manual detection, the method has remarkable advantages in the aspects of detection precision, efficiency and adaptability, can meet the requirements of an industrial production line for real-time detection of submillimeter defects, and provides reliable technical support for intelligent manufacturing quality control.

The present invention relates to a multi-process manufacturing quality prediction method based on an autoencoder and a graph attention network. The method includes: obtaining the time series feature data obtained by the process sensor detection of each process on the production line, constructing a total process data set, inputting it into a semiconductor multi-process quality prediction model, preprocessing the data set, grouping the features according to the number of stages, and inputting the features in each stage into a multi-layer stacked autoencoder, reducing the features to the same dimension to obtain the final intermediate features, and using the intermediate features as the node input of the graph attention network, iteratively updating to obtain the final node output, wherein the graph attention network adopts a residual mechanism, and uses the final node output as the input of the final feedforward neural network to obtain quality feature regression prediction. The present invention realizes multi-dimensional and multi-stage modeling by combining autoencoder feature dimensionality reduction technology with graph neural network data mining technology.

The invention discloses an additive manufacturing powder bed preheating system and method based on hot air-partition radiation synergy. The additive manufacturing powder bed preheating system comprises a preheating outer box, a powder bed, a hot air circulating system, a partition radiation heating system and a layering synergy controller. The method has the beneficial effects that an upper-layer steady-state optimization and lower-layer dynamic compensation framework is adopted, wherein the upper layer aims at energy efficiency and temperature uniformity, and the temperature and the air speed of a hot air inlet are optimized; and the lower layer is based on real-time temperature feedback, the partition radiation power is adjusted through a feedback + feedforward decoupling algorithm, local temperature disturbance is compensated, hot air rapidity and partition radiation accuracy are fused, the problems of response lag, space coupling and laser preheating cooperation deficiency are solved, rapid, uniform and dynamic regulation and control of the temperature field of the powder bed are achieved, the additive manufacturing quality is improved, and the production efficiency is improved. The method is suitable for high-precision manufacturing of various high polymer materials.

The invention discloses an efficient welding method for an exhaust silencer of a motorcycle. According to the invention, breakthrough improvement of manufacturing quality and efficiency is realized through multi-dimensional technical innovation. A high-frequency electromagnetic field resonance stress relief technology is creatively introduced, the metal crystallization behavior is intervened in real time in the welding process, and the thermodynamic equilibrium state of traditional welding is fundamentally reconstructed. Electromagnetic energy directly acts on molten metal in a microscopic particle vibration mode, lattice defects are promoted to be spontaneously repaired in the liquid stage, the deformation cracking risk caused by thermal stress accumulation in a traditional technology is avoided, and microstructure optimization is more synchronously completed. According to the dynamic energy field regulation and control means, the welding strength and toughness of the complex curved surface structure of the silencer are synergistically enhanced, a uniform and compact fine grain structure is formed in a welding seam area, the anti-fatigue performance of a product under the impact of high-temperature tail gas is remarkably improved, meanwhile, the necessary annealing procedure after traditional welding is integrated into in-situ treatment, and the production efficiency is improved. And the production period is greatly shortened.

The embodiment of the invention discloses a personalized ear mold integrated manufacturing method based on multi-material digital light processing, and relates to the technical field of additive manufacturing. The method comprises the following steps: acquiring a personalized ear mold three-dimensional numerical model of a specific patient; adding a static load based on the dead weight of the hearing aid and a dynamic load based on the periodic force of the chewing motion to the numerical model, and performing dynamic stress analysis on the numerical model based on the added loads; based on a stress analysis result, dividing material areas with different hardness in the numerical model, including a rigid supporting area, a flexible sealing area and a transition area; according to the stress gradient line, the volume fraction of the rigid material and the volume fraction of the flexible material used for DLP three-dimensional printing at all positions in the transition area are determined; and according to the volume fraction and the printing energy density of the rigid supporting area and the printing energy density of the flexible sealing area, the printing energy density of all the positions in the transition area is determined. According to the embodiment, the manufacturing quality and efficiency of the ear mold can be improved.

The invention discloses a surface mounting technology (SMT) material feeding mistake proofing method, belongs to the field of electronic manufacturing SMT material feeding management and control, and solves the problems of material mismatching, inaccurate information, nonstandard operation and the like in the SMT material feeding process. According to the method, an SMT material feeding database is constructed, and a material neat set information database and a material feeding information database are covered; creating a feeding operation specification database, and defining an operation specification data set; a voice control instruction database is established, and a feeding operation checking and executing instruction set is arranged. Through cooperation of multiple databases, materials and operation processes are accurately controlled. The invention further provides a material feeding system based on the SMT material feeding mistake proofing method. The automatic feeding device is used for the SMT production line, accurately manages material feeding, reduces the risk of wrong materials, improves the production efficiency, guarantees the manufacturing quality of electronic products, and reduces the production cost.